
This paper presents a first attempt towards utilizing incremental learning to periodically screen different pulmonary disorders from the CXRs irrespective of their scanner specifications.

Unlike state-of-the-art incremental learning approaches [1]}, [2]}, the proposed framework analyzes the structural and semantic relationships between periodically learned chest abnormalities (via proposed \(L_{IL}\)  loss function), which enables it to recognize them effectively at the inference stage.

The proposed framework is rigorously evaluated on five public datasets, where it achieved the accuracy and F1 score of up to 0.8405 and 0.8303, respectively. Furthermore, it outperforms the state-of-the-art schemes on all five datasets, as evident from the Section .

